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Uzbekistan | Information Technology | Volume 14 Issue 12, December 2025 | Pages: 447 - 449
Assessing the Impact and Implications of AI-Driven Code Generation and Review: An Empirical and Legal-Scientific Analysis
Abstract: This paper investigates the impact of Large Language Models (LLMs) on developer productivity and software development processes, focusing on the legal and procedural challenges introduced by AI-generated code. The research problem centers on the tension between demonstrable productivity gains and the risks related to intellectual property, liability, security, and auditability. Employing a mixed-methods approach, this study synthesizes findings from a systematic literature review of recent empirical studies and a doctrinal legal analysis of relevant U.S. copyright and tort law principles. Key findings indicate that while LLM-assisted tools like GitHub Copilot can enhance task completion speed, these benefits are contingent on task complexity and are offset by significant verification overhead and security concerns. Legally, the integration of AI-generated code complicates provenance chains and liability attribution, challenging established doctrines of fair use and the standard of care for software engineers. This study?s significance lies in its interdisciplinary synthesis, providing a structured, evidence-based framework for researchers, legal scholars, and practitioners to navigate the adoption of AI coding assistants. It concludes by proposing a tripartite governance framework encompassing enhanced process integration, traceability protocols, and contractual clarity to mitigate risks while harnessing productivity benefits.
Keywords: AI-generated code, developer productivity, legal liability, GitHub Copilot, software verification
How to Cite?: Dr. Andrei Dragunov, Dr. Anna Tomskova, "Assessing the Impact and Implications of AI-Driven Code Generation and Review: An Empirical and Legal-Scientific Analysis", Volume 14 Issue 12, December 2025, International Journal of Science and Research (IJSR), Pages: 447-449, https://www.ijsr.net/getabstract.php?paperid=SR251203172252, DOI: https://dx.doi.org/10.21275/SR251203172252